On the mechanism of anti-CD39 immune checkpoint therapy
Bibliographic record
Abstract
With the coming of age of cancer immunotherapy, the search for new therapeutic targets has led to the identification of immunosuppressive adenosine as an important regulator of antitumor immunity. This resulted in the development of selective inhibitors targeting various components of the adenosinergic pathway, including small molecules antagonists targeting the high affinity A2A adenosine receptor and low affinity A2B receptor, therapeutic monoclonal antibodies (mAbs) and small molecules targeting CD73 and therapeutic mAbs targeting CD39. As each regulator of the adenosinergic pathway present non-overlapping biologic functions, a better understanding of the mechanisms of action of each targeted approach should accelerate clinical translation and improve rational design of combination treatments. In this review, we discuss the potential mechanisms-of-action of anti-CD39 cancer therapy and potential toxicities that may emerge from sustained CD39 inhibition. Caution should be taken, however, in extrapolating data from gene-targeted mice to patients treated with blocking anti-CD39 agents. As phase I clinical trials are now underway, further insights into the mechanism of action and potential adverse events associated with anti-CD39 therapy are anticipated in coming years.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".